Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations6901265
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory796.4 MiB
Average record size in memory121.0 B

Variable types

Categorical3
Numeric7
Text3
Boolean9

Alerts

Severity is highly imbalanced (56.2%) Imbalance
Amenity is highly imbalanced (90.3%) Imbalance
Give_Way is highly imbalanced (95.8%) Imbalance
Junction is highly imbalanced (62.4%) Imbalance
No_Exit is highly imbalanced (97.4%) Imbalance
Railway is highly imbalanced (92.9%) Imbalance
Stop is highly imbalanced (81.7%) Imbalance
Traffic_Calming is highly imbalanced (98.9%) Imbalance
Duration_Seconds is highly skewed (γ1 = 56.22205817) Skewed
Distance(mi) has 2931021 (42.5%) zeros Zeros
Wind_Speed(mph) has 915200 (13.3%) zeros Zeros

Reproduction

Analysis started2024-11-04 23:43:16.089487
Analysis finished2024-11-04 23:48:09.506524
Duration4 minutes and 53.42 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Severity
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
2
5538813 
3
1128444 
4
 
172262
1
 
61746

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Length

2024-11-05T00:48:09.648652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:48:09.805645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Distance(mi)
Real number (ℝ)

Zeros 

Distinct21687
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5644842
Minimum0
Maximum441.75
Zeros2931021
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:09.945462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.034
Q30.47
95-th percentile2.679
Maximum441.75
Range441.75
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation1.7655502
Coefficient of variation (CV)3.1277229
Kurtosis1602.9111
Mean0.5644842
Median Absolute Deviation (MAD)0.034
Skewness19.967062
Sum3895655
Variance3.1171674
MonotonicityNot monotonic
2024-11-05T00:48:10.101775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2931021
42.5%
0.01 211640
 
3.1%
0.008 13340
 
0.2%
0.009 12678
 
0.2%
0.007 11258
 
0.2%
0.009999999776 11065
 
0.2%
0.011 10572
 
0.2%
0.03 10343
 
0.1%
0.024 9985
 
0.1%
0.029 9896
 
0.1%
Other values (21677) 3669467
53.2%
ValueCountFrequency (%)
0 2931021
42.5%
0.001 4317
 
0.1%
0.002 2459
 
< 0.1%
0.003 3673
 
0.1%
0.004 5607
 
0.1%
0.005 7429
 
0.1%
0.006 9167
 
0.1%
0.007 11258
 
0.2%
0.008 13340
 
0.2%
0.009 12678
 
0.2%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
254.3999939 1
< 0.1%
251.2200012 1
< 0.1%
242.3399963 1
< 0.1%
224.5899963 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%
193.4799957 1
< 0.1%
183.1199951 1
< 0.1%

Street
Text

Distinct320207
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:10.972023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length59
Median length47
Mean length11.061614
Min length1

Characters and Unicode

Total characters76339130
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126725 ?
Unique (%)1.8%

Sample

1st rowState Route 32
2nd rowI-75 S
3rd rowMiamisburg Centerville Rd
4th rowWesterville Rd
5th rowN Woodward Ave
ValueCountFrequency (%)
n 1070416
 
6.2%
s 1067855
 
6.2%
rd 1035170
 
6.0%
w 842301
 
4.9%
e 831585
 
4.9%
st 617863
 
3.6%
ave 579195
 
3.4%
blvd 309195
 
1.8%
dr 296470
 
1.7%
fwy 282591
 
1.6%
Other values (71822) 10205531
59.5%
2024-11-05T00:48:11.533129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11782569
 
15.4%
e 4255206
 
5.6%
a 3395080
 
4.4%
r 2898786
 
3.8%
t 2873613
 
3.8%
S 2667274
 
3.5%
o 2665140
 
3.5%
n 2657983
 
3.5%
d 2489396
 
3.3%
l 2471132
 
3.2%
Other values (70) 38182951
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76339130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11782569
 
15.4%
e 4255206
 
5.6%
a 3395080
 
4.4%
r 2898786
 
3.8%
t 2873613
 
3.8%
S 2667274
 
3.5%
o 2665140
 
3.5%
n 2657983
 
3.5%
d 2489396
 
3.3%
l 2471132
 
3.2%
Other values (70) 38182951
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76339130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11782569
 
15.4%
e 4255206
 
5.6%
a 3395080
 
4.4%
r 2898786
 
3.8%
t 2873613
 
3.8%
S 2667274
 
3.5%
o 2665140
 
3.5%
n 2657983
 
3.5%
d 2489396
 
3.3%
l 2471132
 
3.2%
Other values (70) 38182951
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76339130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11782569
 
15.4%
e 4255206
 
5.6%
a 3395080
 
4.4%
r 2898786
 
3.8%
t 2873613
 
3.8%
S 2667274
 
3.5%
o 2665140
 
3.5%
n 2657983
 
3.5%
d 2489396
 
3.3%
l 2471132
 
3.2%
Other values (70) 38182951
50.0%
Distinct774620
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:12.635689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length6.4827506
Min length5

Characters and Unicode

Total characters44739180
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique434113 ?
Unique (%)6.3%

Sample

1st row45176
2nd row45417
3rd row45459
4th row43081
5th row45417-2476
ValueCountFrequency (%)
91761 10471
 
0.2%
91706 8571
 
0.1%
92507 8001
 
0.1%
92407 7887
 
0.1%
33186 7750
 
0.1%
32819 7186
 
0.1%
33169 6515
 
0.1%
75243 6493
 
0.1%
92324 6204
 
0.1%
90805 6156
 
0.1%
Other values (774610) 6826031
98.9%
2024-11-05T00:48:13.631475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5615634
12.6%
2 5484006
12.3%
3 5135505
11.5%
1 5087549
11.4%
9 3955798
8.8%
7 3873580
8.7%
5 3773880
8.4%
4 3721190
8.3%
6 3083833
6.9%
8 2956082
6.6%
Other values (3) 2052123
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44739180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5615634
12.6%
2 5484006
12.3%
3 5135505
11.5%
1 5087549
11.4%
9 3955798
8.8%
7 3873580
8.7%
5 3773880
8.4%
4 3721190
8.3%
6 3083833
6.9%
8 2956082
6.6%
Other values (3) 2052123
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44739180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5615634
12.6%
2 5484006
12.3%
3 5135505
11.5%
1 5087549
11.4%
9 3955798
8.8%
7 3873580
8.7%
5 3773880
8.4%
4 3721190
8.3%
6 3083833
6.9%
8 2956082
6.6%
Other values (3) 2052123
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44739180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5615634
12.6%
2 5484006
12.3%
3 5135505
11.5%
1 5087549
11.4%
9 3955798
8.8%
7 3873580
8.7%
5 3773880
8.4%
4 3721190
8.3%
6 3083833
6.9%
8 2956082
6.6%
Other values (3) 2052123
 
4.6%

Temperature(F)
Real number (ℝ)

Distinct829
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.960111
Minimum-45
Maximum196
Zeros2574
Zeros (%)< 0.1%
Negative17638
Negative (%)0.3%
Memory size105.3 MiB
2024-11-05T00:48:13.778905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile28
Q150
median64
Q376
95-th percentile89
Maximum196
Range241
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.039681
Coefficient of variation (CV)0.30728933
Kurtosis-0.0065594547
Mean61.960111
Median Absolute Deviation (MAD)13
Skewness-0.52706124
Sum4.2760315 × 108
Variance362.50946
MonotonicityNot monotonic
2024-11-05T00:48:13.924479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 159649
 
2.3%
77 158016
 
2.3%
72 149860
 
2.2%
68 148989
 
2.2%
75 148139
 
2.1%
70 144354
 
2.1%
79 138701
 
2.0%
63 138398
 
2.0%
64 136710
 
2.0%
66 133943
 
1.9%
Other values (819) 5444506
78.9%
ValueCountFrequency (%)
-45 1
 
< 0.1%
-38 3
 
< 0.1%
-36 2
 
< 0.1%
-35 9
< 0.1%
-33 1
 
< 0.1%
-30 1
 
< 0.1%
-29 8
< 0.1%
-28 5
< 0.1%
-27.9 12
< 0.1%
-27.4 3
 
< 0.1%
ValueCountFrequency (%)
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
162 2
 
< 0.1%
161.6 1
 
< 0.1%
143.6 1
 
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.396618
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:14.078340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q148
median66
Q384
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.742039
Coefficient of variation (CV)0.35315581
Kurtosis-0.72765416
Mean64.396618
Median Absolute Deviation (MAD)18
Skewness-0.37822121
Sum4.4441812 × 108
Variance517.20036
MonotonicityNot monotonic
2024-11-05T00:48:14.218002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 258266
 
3.7%
100 240251
 
3.5%
87 153111
 
2.2%
90 150704
 
2.2%
89 125416
 
1.8%
96 117802
 
1.7%
81 114953
 
1.7%
84 114935
 
1.7%
82 112275
 
1.6%
86 109189
 
1.6%
Other values (90) 5404363
78.3%
ValueCountFrequency (%)
1 44
 
< 0.1%
2 187
 
< 0.1%
3 641
 
< 0.1%
4 2023
 
< 0.1%
5 3883
 
0.1%
6 5641
0.1%
7 7485
0.1%
8 8894
0.1%
9 10291
0.1%
10 12508
0.2%
ValueCountFrequency (%)
100 240251
3.5%
99 12525
 
0.2%
98 5980
 
0.1%
97 77544
 
1.1%
96 117802
1.7%
95 8330
 
0.1%
94 102755
 
1.5%
93 258266
3.7%
92 59033
 
0.9%
91 33914
 
0.5%

Pressure(in)
Real number (ℝ)

Distinct1126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.519031
Minimum0
Maximum58.63
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:14.383321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.91
Q129.34
median29.84
Q330.02
95-th percentile30.24
Maximum58.63
Range58.63
Interquartile range (IQR)0.68

Descriptive statistics

Standard deviation1.0090751
Coefficient of variation (CV)0.034183882
Kurtosis20.232115
Mean29.519031
Median Absolute Deviation (MAD)0.25
Skewness-3.5722688
Sum2.0371866 × 108
Variance1.0182325
MonotonicityNot monotonic
2024-11-05T00:48:14.536442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 109975
 
1.6%
29.99 107870
 
1.6%
29.94 106728
 
1.5%
30.01 104743
 
1.5%
29.97 100788
 
1.5%
29.91 99909
 
1.4%
30.04 99074
 
1.4%
29.95 98835
 
1.4%
30.03 97640
 
1.4%
30 97444
 
1.4%
Other values (1116) 5878259
85.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.02 1
 
< 0.1%
0.29 2
 
< 0.1%
0.39 1
 
< 0.1%
2.99 5
< 0.1%
3 2
 
< 0.1%
3.01 1
 
< 0.1%
3.04 4
< 0.1%
9.9 2
 
< 0.1%
16.71 1
 
< 0.1%
ValueCountFrequency (%)
58.63 7
< 0.1%
58.39 2
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 4
< 0.1%
58.04 3
< 0.1%
57.74 1
 
< 0.1%
57.54 2
 
< 0.1%
56.54 1
 
< 0.1%
56.31 1
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1090536
Minimum0
Maximum140
Zeros6994
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:14.663995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6414816
Coefficient of variation (CV)0.2899842
Kurtosis80.609317
Mean9.1090536
Median Absolute Deviation (MAD)0
Skewness2.1313248
Sum62863992
Variance6.9774251
MonotonicityNot monotonic
2024-11-05T00:48:14.805120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 5580997
80.9%
7 190662
 
2.8%
9 168643
 
2.4%
8 133938
 
1.9%
5 127469
 
1.8%
6 113352
 
1.6%
2 112662
 
1.6%
4 107172
 
1.6%
3 106175
 
1.5%
1 95411
 
1.4%
Other values (71) 164784
 
2.4%
ValueCountFrequency (%)
0 6994
 
0.1%
0.06 311
 
< 0.1%
0.1 865
 
< 0.1%
0.12 1704
 
< 0.1%
0.19 40
 
< 0.1%
0.2 7134
 
0.1%
0.25 25633
0.4%
0.31 4
 
< 0.1%
0.38 316
 
< 0.1%
0.4 51
 
< 0.1%
ValueCountFrequency (%)
140 1
 
< 0.1%
111 3
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 46
 
< 0.1%
98 1
 
< 0.1%
90 12
 
< 0.1%
80 288
< 0.1%
78 1
 
< 0.1%
76 3
 
< 0.1%

Wind_Direction
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
CALM
915192 
S
 
404009
SSW
 
372557
W
 
368759
WNW
 
365224
Other values (18)
4475524 

Length

Max length8
Median length5
Mean length2.770933
Min length1

Characters and Unicode

Total characters19122943
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowSW
3rd rowSW
4th rowSSW
5th rowWSW

Common Values

ValueCountFrequency (%)
CALM 915192
 
13.3%
S 404009
 
5.9%
SSW 372557
 
5.4%
W 368759
 
5.3%
WNW 365224
 
5.3%
NW 356045
 
5.2%
SW 353083
 
5.1%
WSW 342045
 
5.0%
SSE 337450
 
4.9%
NNW 320915
 
4.7%
Other values (13) 2765986
40.1%

Length

2024-11-05T00:48:14.946440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 915192
 
13.3%
s 404009
 
5.9%
ssw 372557
 
5.4%
w 368759
 
5.3%
wnw 365224
 
5.3%
nw 356045
 
5.2%
sw 353083
 
5.1%
wsw 342045
 
5.0%
sse 337450
 
4.9%
nnw 320915
 
4.7%
Other values (13) 2765986
40.1%

Most occurring characters

ValueCountFrequency (%)
W 3347092
17.5%
S 3238611
16.9%
N 2772411
14.5%
E 2506338
13.1%
A 1156947
 
6.1%
M 915192
 
4.8%
L 915192
 
4.8%
C 915192
 
4.8%
t 561163
 
2.9%
V 353115
 
1.8%
Other values (11) 2441690
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19122943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3347092
17.5%
S 3238611
16.9%
N 2772411
14.5%
E 2506338
13.1%
A 1156947
 
6.1%
M 915192
 
4.8%
L 915192
 
4.8%
C 915192
 
4.8%
t 561163
 
2.9%
V 353115
 
1.8%
Other values (11) 2441690
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19122943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3347092
17.5%
S 3238611
16.9%
N 2772411
14.5%
E 2506338
13.1%
A 1156947
 
6.1%
M 915192
 
4.8%
L 915192
 
4.8%
C 915192
 
4.8%
t 561163
 
2.9%
V 353115
 
1.8%
Other values (11) 2441690
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19122943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3347092
17.5%
S 3238611
16.9%
N 2772411
14.5%
E 2506338
13.1%
A 1156947
 
6.1%
M 915192
 
4.8%
L 915192
 
4.8%
C 915192
 
4.8%
t 561163
 
2.9%
V 353115
 
1.8%
Other values (11) 2441690
12.8%

Wind_Speed(mph)
Real number (ℝ)

Zeros 

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6988973
Minimum0
Maximum1087
Zeros915200
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:15.087710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7
Q310.4
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.4065039
Coefficient of variation (CV)0.70224394
Kurtosis1117.1177
Mean7.6988973
Median Absolute Deviation (MAD)3
Skewness8.1183667
Sum53132130
Variance29.230285
MonotonicityNot monotonic
2024-11-05T00:48:15.244695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 915200
 
13.3%
5 512320
 
7.4%
6 496040
 
7.2%
3 492665
 
7.1%
7 461892
 
6.7%
8 415707
 
6.0%
9 374028
 
5.4%
10 311990
 
4.5%
12 269587
 
3.9%
4.6 213643
 
3.1%
Other values (169) 2438193
35.3%
ValueCountFrequency (%)
0 915200
13.3%
1 163
 
< 0.1%
1.2 436
 
< 0.1%
2 417
 
< 0.1%
2.3 882
 
< 0.1%
3 492665
7.1%
3.5 199934
 
2.9%
4.6 213643
 
3.1%
5 512320
7.4%
5.8 211891
 
3.1%
ValueCountFrequency (%)
1087 1
 
< 0.1%
984 1
 
< 0.1%
822.8 7
< 0.1%
812 1
 
< 0.1%
703.1 2
 
< 0.1%
580 2
 
< 0.1%
471.8 1
 
< 0.1%
328 1
 
< 0.1%
255 1
 
< 0.1%
254.3 2
 
< 0.1%
Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:15.417291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length7.6872585
Min length3

Characters and Unicode

Total characters53051808
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowOvercast
2nd rowMostly Cloudy
3rd rowMostly Cloudy
4th rowLight Rain
5th rowOvercast
ValueCountFrequency (%)
fair 2470211
25.7%
cloudy 2418446
25.2%
mostly 964545
 
10.1%
partly 660362
 
6.9%
clear 610683
 
6.4%
light 508377
 
5.3%
rain 474794
 
4.9%
overcast 319799
 
3.3%
scattered 175020
 
1.8%
clouds 175020
 
1.8%
Other values (48) 819626
 
8.5%
2024-11-05T00:48:15.763618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 4865647
 
9.2%
a 4846338
 
9.1%
r 4382400
 
8.3%
y 4225496
 
8.0%
o 3867241
 
7.3%
i 3710765
 
7.0%
C 3204165
 
6.0%
d 2938459
 
5.5%
t 2917761
 
5.5%
2695618
 
5.1%
Other values (36) 15397918
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53051808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 4865647
 
9.2%
a 4846338
 
9.1%
r 4382400
 
8.3%
y 4225496
 
8.0%
o 3867241
 
7.3%
i 3710765
 
7.0%
C 3204165
 
6.0%
d 2938459
 
5.5%
t 2917761
 
5.5%
2695618
 
5.1%
Other values (36) 15397918
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53051808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 4865647
 
9.2%
a 4846338
 
9.1%
r 4382400
 
8.3%
y 4225496
 
8.0%
o 3867241
 
7.3%
i 3710765
 
7.0%
C 3204165
 
6.0%
d 2938459
 
5.5%
t 2917761
 
5.5%
2695618
 
5.1%
Other values (36) 15397918
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53051808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 4865647
 
9.2%
a 4846338
 
9.1%
r 4382400
 
8.3%
y 4225496
 
8.0%
o 3867241
 
7.3%
i 3710765
 
7.0%
C 3204165
 
6.0%
d 2938459
 
5.5%
t 2917761
 
5.5%
2695618
 
5.1%
Other values (36) 15397918
29.0%

Amenity
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6815353 
True
 
85912
ValueCountFrequency (%)
False 6815353
98.8%
True 85912
 
1.2%
2024-11-05T00:48:15.889118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Crossing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6109696 
True
791569 
ValueCountFrequency (%)
False 6109696
88.5%
True 791569
 
11.5%
2024-11-05T00:48:15.967757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Give_Way
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6869396 
True
 
31869
ValueCountFrequency (%)
False 6869396
99.5%
True 31869
 
0.5%
2024-11-05T00:48:16.062281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Junction
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6399773 
True
 
501492
ValueCountFrequency (%)
False 6399773
92.7%
True 501492
 
7.3%
2024-11-05T00:48:16.156536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

No_Exit
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6883495 
True
 
17770
ValueCountFrequency (%)
False 6883495
99.7%
True 17770
 
0.3%
2024-11-05T00:48:16.243836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Railway
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6841971 
True
 
59294
ValueCountFrequency (%)
False 6841971
99.1%
True 59294
 
0.9%
2024-11-05T00:48:16.329071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Stop
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6709990 
True
 
191275
ValueCountFrequency (%)
False 6709990
97.2%
True 191275
 
2.8%
2024-11-05T00:48:16.407783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Traffic_Calming
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
6894414 
True
 
6851
ValueCountFrequency (%)
False 6894414
99.9%
True 6851
 
0.1%
2024-11-05T00:48:16.486049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
False
5881403 
True
1019862 
ValueCountFrequency (%)
False 5881403
85.2%
True 1019862
 
14.8%
2024-11-05T00:48:16.580316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
Day
5160127 
Night
1741138 

Length

Max length5
Median length3
Mean length3.5045852
Min length3

Characters and Unicode

Total characters24186071
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowDay
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 5160127
74.8%
Night 1741138
 
25.2%

Length

2024-11-05T00:48:16.721658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:48:16.831712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
day 5160127
74.8%
night 1741138
 
25.2%

Most occurring characters

ValueCountFrequency (%)
D 5160127
21.3%
a 5160127
21.3%
y 5160127
21.3%
N 1741138
 
7.2%
i 1741138
 
7.2%
g 1741138
 
7.2%
h 1741138
 
7.2%
t 1741138
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24186071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5160127
21.3%
a 5160127
21.3%
y 5160127
21.3%
N 1741138
 
7.2%
i 1741138
 
7.2%
g 1741138
 
7.2%
h 1741138
 
7.2%
t 1741138
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24186071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5160127
21.3%
a 5160127
21.3%
y 5160127
21.3%
N 1741138
 
7.2%
i 1741138
 
7.2%
g 1741138
 
7.2%
h 1741138
 
7.2%
t 1741138
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24186071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5160127
21.3%
a 5160127
21.3%
y 5160127
21.3%
N 1741138
 
7.2%
i 1741138
 
7.2%
g 1741138
 
7.2%
h 1741138
 
7.2%
t 1741138
 
7.2%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct72892
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23931.489
Minimum120
Maximum1.6877634 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T00:48:16.957887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile1719
Q12014
median4500
Q37506
95-th percentile21600
Maximum1.6877634 × 108
Range1.6877622 × 108
Interquartile range (IQR)5492

Descriptive statistics

Standard deviation758788.58
Coefficient of variation (CV)31.706702
Kurtosis4638.6566
Mean23931.489
Median Absolute Deviation (MAD)2704
Skewness56.222058
Sum1.6515755 × 1011
Variance5.7576012 × 1011
MonotonicityNot monotonic
2024-11-05T00:48:17.130581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 295021
 
4.3%
1800 95582
 
1.4%
2700 58150
 
0.8%
4500 56342
 
0.8%
3600 51550
 
0.7%
14400 49404
 
0.7%
1786 46589
 
0.7%
1785 46544
 
0.7%
1787 45507
 
0.7%
1784 45129
 
0.7%
Other values (72882) 6111447
88.6%
ValueCountFrequency (%)
120 2
 
< 0.1%
150 3
 
< 0.1%
152 1
 
< 0.1%
180 16
< 0.1%
210 6
 
< 0.1%
221 1
 
< 0.1%
229 1
 
< 0.1%
240 12
< 0.1%
270 5
 
< 0.1%
271 1
 
< 0.1%
ValueCountFrequency (%)
168776340 2
< 0.1%
134184345 1
 
< 0.1%
134181332 3
< 0.1%
134179838 3
< 0.1%
134176830 2
< 0.1%
106135755 1
 
< 0.1%
100954757 1
 
< 0.1%
94755540 1
 
< 0.1%
94697995 1
 
< 0.1%
94697990 1
 
< 0.1%

Interactions

2024-11-05T00:47:30.294079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:33.522647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:41.401238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:49.565892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:06.836864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:14.686307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:22.596185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:31.470801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:34.552473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:42.519507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:50.766240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:07.997733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:15.800209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:23.712420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:32.586100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:35.674509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:43.725355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:59.916600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:09.001890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:16.913404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:24.802026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:33.782576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:36.767732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:44.876416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:01.892018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:10.090780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:18.027966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:25.909768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:34.946743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:37.884505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:46.030103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:03.218436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:11.245110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:19.126545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:26.954331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:36.085113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:39.067785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:47.222451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:04.448576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:12.374129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:20.245016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:28.009150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:37.148504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:40.242281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:46:48.367292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:05.678351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:13.567481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:21.473982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:47:29.188984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T00:48:17.272447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Wind_DirectionWind_Speed(mph)
Amenity1.0000.0060.1480.0000.0010.0060.0150.0260.0140.0230.0500.0380.0340.0100.0230.1040.0020.0220.000
Civil_Twilight0.0061.0000.0380.0030.0070.0050.2430.0140.0040.0340.0000.0560.0010.2780.0000.0440.0150.1800.001
Crossing0.1480.0381.0000.0040.0070.0580.0350.0880.0620.0350.1790.1210.1190.0610.0370.4750.0070.0540.001
Distance(mi)0.0000.0030.0041.0000.3910.000-0.0130.0000.000-0.1060.0000.0050.002-0.0580.0010.004-0.0120.001-0.002
Duration_Seconds0.0010.0070.0070.3911.0000.001-0.0130.0080.000-0.1140.0010.0090.003-0.0300.0000.0080.0050.005-0.036
Give_Way0.0060.0050.0580.0000.0011.0000.0040.0090.0070.0080.0030.0080.0300.0050.0030.0720.0070.0100.000
Humidity(%)0.0150.2430.035-0.013-0.0130.0041.0000.0090.0090.0430.0060.0270.026-0.3310.0050.021-0.4650.084-0.198
Junction0.0260.0140.0880.0000.0080.0090.0091.0000.0040.0270.0090.0530.0360.0270.0050.1040.0040.0300.000
No_Exit0.0140.0040.0620.0000.0000.0070.0090.0041.0000.0070.0040.0120.0260.0090.0130.0300.0070.0060.000
Pressure(in)0.0230.0340.035-0.106-0.1140.0080.0430.0270.0071.0000.0160.0420.0030.0180.0050.0340.0780.0780.001
Railway0.0500.0000.1790.0000.0010.0030.0060.0090.0040.0161.0000.0140.0070.0100.0050.0590.0030.0070.000
Severity0.0380.0560.1210.0050.0090.0080.0270.0530.0120.0420.0141.0000.0590.0380.0060.1200.0120.0960.001
Stop0.0340.0010.1190.0020.0030.0300.0260.0360.0260.0030.0070.0591.0000.0160.0270.0480.0010.0090.000
Temperature(F)0.0100.2780.061-0.058-0.0300.005-0.3310.0270.0090.0180.0100.0380.0161.0000.0060.0470.2290.0830.087
Traffic_Calming0.0230.0000.0370.0010.0000.0030.0050.0050.0130.0050.0050.0060.0270.0061.0000.0110.0020.0060.000
Traffic_Signal0.1040.0440.4750.0040.0080.0720.0210.1040.0300.0340.0590.1200.0480.0470.0111.0000.0020.0750.002
Visibility(mi)0.0020.0150.007-0.0120.0050.007-0.4650.0040.0070.0780.0030.0120.0010.2290.0020.0021.0000.0110.055
Wind_Direction0.0220.1800.0540.0010.0050.0100.0840.0300.0060.0780.0070.0960.0090.0830.0060.0750.0111.0000.002
Wind_Speed(mph)0.0000.0010.001-0.002-0.0360.000-0.1980.0000.0000.0010.0000.0010.0000.0870.0000.0020.0550.0021.000

Missing values

2024-11-05T00:47:38.886245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T00:47:47.493955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
220.01State Route 324517636.0100.029.6710.0SW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseTrueNight1800.0
330.01I-75 S4541735.196.029.649.0SW4.6Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
420.01Miamisburg Centerville Rd4545936.089.029.656.0SW3.5Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseTrueDay1800.0
530.01Westerville Rd4308137.997.029.637.0SSW3.5Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
620.00N Woodward Ave45417-247634.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
730.01N Main St4540534.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
820.00Notre Dame Ave45404-192333.399.029.675.0SW1.2Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
930.01Westerville Rd4308137.4100.029.623.0SSW4.6Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
1030.01Outerbelt S4322835.693.029.645.0WNW5.8RainFalseTrueFalseTrueFalseFalseFalseFalseFalseDay1800.0
1130.01I-70 E4306837.4100.029.623.0SSW4.6Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
772838420.390I-10 E9170678.052.029.6910.0VAR6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1723.0
772838520.000CA-60 E9255588.032.028.2010.0WNW10.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1703.0
772838620.189El Camino Real N9300373.068.029.7610.0W9.0FairFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1703.0
772838720.443Santa Ana Fwy S9278075.060.029.7410.0SSW9.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838820.000Golden State Fwy N9133181.048.028.7810.0ESE6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838920.543Pomona Fwy E9250186.040.028.9210.0W13.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1716.0
772839020.338I-8 W9210870.073.029.3910.0SW6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1613.0
772839120.561Garden Grove Fwy9286673.064.029.7410.0SSW10.0Partly CloudyFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1708.0
772839220.772San Diego Fwy S9023071.081.029.6210.0SW8.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1761.0
772839320.537CA-210 W9234679.047.028.637.0SW7.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1765.0